Abstract: The diversity of crowd scale in reality is a great challenge to crowd counting algorithms. Therefore, a novel crowd counting algorithm based on scale fusion is proposed in this study. Firstly, the algorithm for density map generation is optimized. Multiple head detectors are used to obtain part of the head scales of the sparse crowd, and RBF interpolation is employed to complete this part of the density map. As to the dense part of crowd, the traditional distance self-adaptive algorithm is adopted to generate a more accurate density map. Secondly, the regression neural network of the density map is designed with a mobile inverted bottleneck convolution module, and a dilated convolution module is added to facilitate the extraction of head edge features. Finally, the loss function of the regression neural network is optimized by distinguishing the crowd area from the non-crowd area. In the experiment part, the algorithm is compared with other similar algorithms on multiple datasets, and the results show that the proposed method can significantly improve the accuracy of crowd counting.
Abstract: The device fault generally starts from a minor one and gradually develops to the loss of working capacity of the whole set. Detection in case of a minor fault can recover the unnecessary loss. Therefore, this study proposes a method to evaluate device health status on the basis of the Weighted Mahalanobis Distance (WMD) and the Device Status Index (DSI). Based on an improved Mahalanobis-Taguchi system, the method constructs a stable reference space for the characteristic parameters during the effective operation of the device. It selects the characteristics and calculates the WMDaccording to the device fault sensitivity, eliminating the interference of characteristic correlation. Then Box-Cox transformation is used to determine the threshold value of the DSI to build a health status model of the complex heavy device, and the model is verified by experiments. The WMD values of the normal samples are all below the fault threshold, and nearly 98.6% of the sample values are within the warning signs. The proposed method can provide data support for maintenance and management of complex heavy devices, thereby facilitating industrial production.
Abstract: As the intelligence level grows, a large amount of new knowledge is generated all the time, and knowledge graph has gradually become one of the tools for knowledge management. However, the existing knowledge graph still has some problems, such as missing attributes, sparse relations, and massive noisy information, which leads to poor graph quality and is easy to affect various tasks in the field of natural language processing. As a research hotspot, the knowledge reasoning technology oriented to the knowledge graph is the main method to solve this problem. It improves the information of the knowledge graph by simulating the human reasoning process, with a good performance in many applications. Taking the knowledge graph as the pointcut, this study classifies and explains the knowledge reasoning technology by categories and elaborates on several application tasks of the technology, such as intelligent question-answering and the recommendation system. Finally, it forecasts the main research directions in the future and puts forward several research ideas.
Abstract: Due to the shaking of a handheld camera or the movement of targets, the video image data is subject to motion blur, which reduces the image quality of human perception. With regard to the problem, from how to obtain clear images from the original process to how to obtain clear images efficiently, a new model for real-time video image deblurring based on the lightweight Generative Adversarial Network (GAN) is proposed in this study. The model defines PatchGAN as a discriminant network and sets up a dual-scale discriminator for global images and local features on the basis of it; the generation network takes lightweight MobileNetV3 as the backbone network and introduces a feature pyramid for feature extraction to solve the problem of low utilization of feature information in the discrimination network and low inference efficiency of the generation network. This model uses an end-to-end approach to efficiently deblur the video image. After experiments on the GoPro and Kohler datasets, the results show that the sharp image deblurred by this model has a high peak signal-to-noise ratio and great structural similarity, and the inference speed reaches 1.7–127 times faster than that of other models.
Abstract: The next Point-Of-Interest (POI) recommendation is one of the most important services of the Location-Based Social Network (LBSN). It can not only help users find the destination which they are interested in, but also improve the potential income of business providers. Existing algorithms have employed user behavior sequences and the POI information for recommendation, but none of them fully utilize POI side information, thereby failing to ease the problems of cold start and sparse data. In light of the above analysis, this study proposed a POI recommendation system, Graph Embedding-Gated Recurrent Unit (GE-GRU). Firstly, GE-GRU relies on Graph Embedding (GE) to integrate the POI itself with its side information to get the POI embedding that contains deep information. Then, the POI embedding is input into the GRU-based neural network to model recent user preferences to acquire user embedding. Finally, according to the POI rank list, the next POI can be recommended. Experiments are conducted on a real dataset, Foursquare, which contains more than 480 000 check-ins, and Accuracy@k is adopted for evaluation. The results show that, compared with GRU and Long Short-Term Memory (LSTM), GE-GRU has 3% and 7% improvement on Accuracy@10, respectively.
Abstract: Streamline rendering has long remained as one of the most common techniques for flow visualization. The streamline is an effective sparse representation of the flow field, which can capture the flow behavior, but generating streamline needs long-term particle tracing and massive integral operations. Large-scale flow visualization takes considerable computation time, and the parallel computing algorithm and high-performance equipment are needed. In this study, a high-resolution streamline generation algorithm based on deep learning is designed. The initial sparse low-resolution streamline is quickly mapped into the dense high-resolution streamline to provide reliable streamline visualization results in a short time. On this basis, an interactive real-time flow visualization system is developed, which is capable of flow-field feature detection, attribute correlation analysis, information theory analysis, etc. It can help users quickly understand the flow field data and find their areas of interest for post-hoc analysis, avoiding redundant data and enhancing work efficiency. In addition, it can meet the users’ needs for multi-dimensional correlation analysis of flow field structures, features, and attributes.
Abstract: With the continuous development of digital twin technology at this stage, research and applications surrounding digital twins have gradually become a hot spot. Because traditional automated driving test methods have various defects in terms of functionality, safety, and test cost, this article proposes a digital twin automatic driving test method based on the basic characteristics of the digital twin and the test method of autonomous driving. The method of constructing the driving test environment uses spatial coordinate mapping, collision detection model, and virtual scene registration to map the automatic driving information in the actual environment to the virtual scene. At the same time, the corresponding mixed reality automatic driving test model is constructed and passed the experiment. The collision test with interactive features of the mixed reality system is shown. The performance of the system at sampling frequencies of 50ms, 200ms and 1000ms is compared and analyzed. Experiments show that the algorithm in this paper has better operating frame rate characteristics at the sampling frequency of 200ms or above.
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